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TL;DR: In this article , a deep collocation method (DCM) was proposed for three-dimensional potential problems in non-homogeneous media, which utilizes a physics-informed neural network with material transfer learning.
Abstract: Abstract In this work, we present a deep collocation method (DCM) for three-dimensional potential problems in non-homogeneous media. This approach utilizes a physics-informed neural network with material transfer learning reducing the solution of the non-homogeneous partial differential equations to an optimization problem. We tested different configurations of the physics-informed neural network including smooth activation functions, sampling methods for collocation points generation and combined optimizers. A material transfer learning technique is utilized for non-homogeneous media with different material gradations and parameters, which enhance the generality and robustness of the proposed method. In order to identify the most influential parameters of the network configuration, we carried out a global sensitivity analysis. Finally, we provide a convergence proof of our DCM. The approach is validated through several benchmark problems, also testing different material variations.
20 citations
TL;DR: In this paper , an efficient multilevel Monte Carlo (MLMC) method for the topology optimization of flexoelectric structures is presented, where GA based integer-valued optimization is used to obtain the optimal topological design.
Abstract: We present an efficient multilevel Monte Carlo (MLMC) method for the topology optimization of flexoelectric structures. A flexoelectric composite consisting of flexoelectric and purely elastic building blocks is investigated. The governing equations are solved by Non-Uniform Rational B-spline (NURBS)-based isogeometric analysis (IGA) exploiting its higher order continuity. Genetic algorithms (GA) based integer-valued optimization is used to obtain the optimal topological design. The uncertainties in the material properties and the volume fraction of the constituents are considered to quantify the uncertainty in the electromechanical coupling effect. Then, a multilevel hierarchy of computational meshes is obtained by a uniform refinement according to a geometric sequence. We estimate the growth rate of the simulation cost, in addition to the rates of decay in the expectation and the variance of the differences between the approximations over the hierarchy. Finally, we determine the minimum number of simulations required on each level to achieve the desired accuracy at different prescribed error tolerances. The results show that the proposed method reduces the computational cost in the numerical experiments without loss of the accuracy. The overall computation saving was in the range 2.0–3.5.
20 citations
TL;DR: In this article, an efficient multilevel Monte Carlo (MLMC) method for the topology optimization of flexoelectric structures is presented, where GA based integer-valued optimization is used to obtain the optimal topological design.
Abstract: We present an efficient multilevel Monte Carlo (MLMC) method for the topology optimization of flexoelectric structures. A flexoelectric composite consisting of flexoelectric and purely elastic building blocks is investigated. The governing equations are solved by Non-Uniform Rational B-spline (NURBS)-based isogeometric analysis (IGA) exploiting its higher order continuity. Genetic algorithms (GA) based integer-valued optimization is used to obtain the optimal topological design. The uncertainties in the material properties and the volume fraction of the constituents are considered to quantify the uncertainty in the electromechanical coupling effect. Then, a multilevel hierarchy of computational meshes is obtained by a uniform refinement according to a geometric sequence. We estimate the growth rate of the simulation cost, in addition to the rates of decay in the expectation and the variance of the differences between the approximations over the hierarchy. Finally, we determine the minimum number of simulations required on each level to achieve the desired accuracy at different prescribed error tolerances. The results show that the proposed method reduces the computational cost in the numerical experiments without loss of the accuracy. The overall computation saving was in the range 2.0–3.5.
20 citations
TL;DR: In this article, the authors studied the problem of a muffler with internal extended inlet/outlet tubes with the aim to optimize its geometric design, and proposed an isogeometric BM (IGA-BM) model to solve the muffler problem as an interior 3D Helmholtz acoustic problem.
16 citations
TL;DR: In this article , a cross-linked chitosan (Chito) by an original sulfonating process allows synthesizing a highly efficient sorbent for Li(I) recovery from acidic solution (at pH ≈2).
14 citations
Authors
Showing all 18 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Justice Kwaku Addo | 5 | 8 | 102 |
| Guohua Xi | 3 | 3 | 24 |
| Laurence W. Corbett | 1 | 1 | 3 |
| Claire-Marie Beyet | 1 | 1 | 1 |
| Jesper Tijmstra | 1 | 1 | 4 |
| Maria Aline Dias Pereira | 1 | 1 | 6 |
| Andrew Dunnett | 0 | 1 | 0 |
| Luana Pereira Gonçalves | 0 | 1 | 0 |
| Jun Xu | 0 | 1 | 0 |
| Emma K. Massey | 0 | 1 | 0 |
| Michelle Peterie | 0 | 1 | 0 |
| Juan Santos | 0 | 1 | 0 |
| sadsad545asd | 0 | 1 | 0 |
| Sujata Patel | 0 | 1 | 0 |
| Sabine Lefebvre | 0 | 1 | 0 |